Quantification of Climate Changes and Human Activities That Impact Runoff in the Taihu Lake Basin, China

Abstract

Although a fragile climate region, the Taihu Lake Basin is among the most developed regions in China and is subjected to intense anthropogenic interference. In this basin, water resources encounter major challenges (e.g., floods, typhoons, and water pollution). In this study, the impacts of climate changes and human activities on hydrological processes were estimated to aid water resource management in developed regions in China. The Mann-Kendall test and cumulative anomaly curve were applied to detect the turning points in the runoff series. The year of 1982 divides the study period (1956~2008) into a baseline period (1956~1981) and a modified period (1982~2008). The double mass curve method and the hydrological sensitivity method based on the Budyko framework were applied to quantitatively attribute the runoff variation to climate changes and human activities. The results demonstrated that human activities are the dominant driving force of runoff variations in the basin, with a contribution of 83~89%; climate changes contributed to 11~17% of the variations. Moreover, the subregions of the basin indicated that humans severely disturbed the runoff variation, with contributions as high as 95~97%.

Dingzhi Peng 1 and Linghua Qiu 2 and Jing Fang 1 and Zhongyuan Zhang 1

Academic Editor:Hua Fan

1, College of Water Sciences, Beijing Normal University, Beijing 100875, China
2, Department of Civil Engineering, University of Hong Kong, Pokfulam, Hong Kong

Received 15 July 2015; Revised 29 December 2015; Accepted 17 January 2016

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Extreme hydrological events, such as floods and droughts, occur frequently [1, 2]. Most hydrological disturbances in basins are due to climatic and anthropogenic factors. Regional climate changes mainly influence precipitation and evapotranspiration, leading to an increase or decrease in regional water resources. Under climate change, complex hydrological uncertainties enhance the risk of disaster [3]. With rapid social and economic development, the intensity of human activities is increasing in most regions and is altering hydrological processes [4-7]. The dual impacts of climate changes and human activities alter the hydrological cycle and introduce water issues. There are many studies that attempt to separate the causes and effects of climate changes and human effects [8, 9]. In some basins, climate changes play a key role in hydrological variations [10], while human activities dominate in the Red River Basin [11] and the Reno River catchment [12]. Actually, the effects of climate changes and human activities interact, but most research simply assumes that the factors are independent.

As a typical region that experiences climatic impacts and intense human interference, the Taihu Lake Basin was chosen as a study case. Climate factors, such as El Niño-Southern Oscillation (ENSO) and the East Asian monsoon, lead to a fluctuating climate in the basin [13]. Considering the strong urbanization, human activities also increase the flood risk [14]. Because of the rapid social and economic development, the demand on water resources is increasing. Therefore, attributing hydrological modifications to these driving forces will have practical significance for the sustainable management of water resources. In this study, the hydrological changes based on trends and turning points were revealed in the Taihu Lake Basin. The impacts of climate changes and human activities on hydrological processes were quantified.

2. Study Area and Materials

The Taihu Lake Basin (Figure 1), with an area of 36 895 km² , is located in the core region of the Yangtze River Delta. The basin has a typical subtropical monsoon climate. The water resource carrying capacity is quite low under climate changes. Moreover, the basin is one of the most developed areas in China. Many artificial river systems and hydraulic projects have been constructed. The underlying surface of the basin has been remarkably altered by human activities.

Figure 1: Taihu Lake Basin.

[figure omitted; refer to PDF]

The annual mean precipitation of the Taihu Lake Basin is approximately 1200 mm. However, the rainfall is concentrated in the flood season (from May to September). The annual mean runoff is nearly 430 mm. In this study, the Taihu Lake Basin was divided into four zones (the administrative units of the Taihu Basin Authority, Ministry of Water Resources, China): the Wuyang Zone (WYZ), the Huxi and Taihu Zone (HTZ), the Hangjiahu Zone (HJHZ), and the Huangpujiang Zone (HPJZ). The hydrological characteristics of the four zones are listed in Table 1. The daily precipitation, temperature, humidity, wind speed, and sunshine duration of 11 meteorological stations (see Figure 1) in the basin from 1956 to 2008 were collected from the China Meteorological Data Sharing Service System (http://data.cma.cn/). The daily potential evapotranspiration was estimated by the Penman-Monteith method [15] based on the meteorological data mentioned above. The annual precipitation and potential evapotranspiration were calculated and then interpolated by the inverse distance weighted method in each zone and the entire basin. The annual runoff of the basin and the four zones during 1956~2008 was provided by the Taihu Basin Authority, Ministry of Water Resources, China, respectively.

Table 1: Hydrological characteristics of the Taihu Lake Basin and its zones during 1956~2008.

Zone	Annual runoff(mm)	Annual precipitation(mm)	Annual potentialevapotranspiration(mm)
Hangjiahu Zone	451.7	1238.8	868.0
Huangpujiang Zone	395.2	1164.9	890.7
Wuyang Zone	373.0	1133.9	881.6
Huxi and Taihu Zone	463.8	1226.6	856.3
Taihu Basin	429.7	1199.5	870.3

3. Methods

3.1. Trend and Turning Point Analysis

The cumulative departure curve was used to analyze the trends in the hydrological time series and to detect the preliminary turning points [13]. Then, the Mann-Kendall method [16-18] was employed to verify the turning points. The turning point was applied to divide the data series into two periods: the baseline period and the modified period. In the baseline period, the status or condition of the basin is assumed natural, without significant human interference (i.e., the reference period for the modified period). In contrast, human activities in the modified period are very apparent.

The departures of the cumulative departure curve are measured as follows [13]: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the time series. The extreme values of the cumulative departure curves are probably the turning points.

The MK method [16, 17] has been widely used to detect the trends and turning points of time series [18]. For an independent time series [figure omitted; refer to PDF] , [figure omitted; refer to PDF] is established: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] . In the equation, two observations are [figure omitted; refer to PDF] and [figure omitted; refer to PDF] ( [figure omitted; refer to PDF] ). If [figure omitted; refer to PDF] , then [figure omitted; refer to PDF] ; if [figure omitted; refer to PDF] , then [figure omitted; refer to PDF] ; and if [figure omitted; refer to PDF] , then [figure omitted; refer to PDF] . The statistical variable [figure omitted; refer to PDF] has a standard normal distribution: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] ; [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the variance and mean, respectively, of [figure omitted; refer to PDF] . If the time series have the same continuous distribution, then [figure omitted; refer to PDF]

If [figure omitted; refer to PDF] for a given significance level [figure omitted; refer to PDF] , then the statistical series exhibits an obvious trend. Then, the process was repeated by the inverse time series ( [figure omitted; refer to PDF] ) with [figure omitted; refer to PDF] ( [figure omitted; refer to PDF] ) and [figure omitted; refer to PDF] . Finally, the intersection of the two graphs ( [figure omitted; refer to PDF] ) is considered the turning point if it is within the critical lines.

3.2. Double Mass Curve Method

The runoff variation was attributed to impacts of both climate changes and human activities. The total runoff variation was calculated as follows: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the runoff variation induced by climate changes and human activities, respectively. [figure omitted; refer to PDF] is the total runoff variation, which could also be calculated as follows: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the annual mean runoff. Subscripts 1 and 2 indicate the baseline period and modified period, respectively. Then, the contributions of climate changes and human activities to runoff variations could be estimated by [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] represent the contribution of climate changes and human activities, respectively.

The double mass curve method [19] employs linear regression analysis of hydrological time series. The relationship between the cumulative runoff and cumulative precipitation in the baseline period is analyzed by [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the runoff and precipitation, respectively. [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are two parameters. Then, the regression equation is used to simulate the runoff in the modified period. Since the climate factor, precipitation, is the same for both the simulated and measured runoff, which eliminates the influence of climate changes, the difference between the mean simulated and measured runoff is the runoff variation induced by human activities: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the annual mean simulated runoff. Then, the runoff variation induced by climate change is the difference between the total runoff variation and the runoff variation induced by human activities.

3.3. Hydrological Sensitivity Method Based on the Budyko Framework

The hydrological sensitivity method could be applied to estimate the impact of climate changes on hydrological processes based on the water balance [20, 21]: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the evapotranspiration and [figure omitted; refer to PDF] is the variation in the water storage in the basin, which can be neglected for 5~10 years. Then, (10) could be transformed based on the relationship between evapotranspiration and potential evapotranspiration [22]: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the potential evapotranspiration and [figure omitted; refer to PDF] is a landscape parameter; [figure omitted; refer to PDF] indicates the dryness index, that is, [figure omitted; refer to PDF] . Then, the runoff variation induced by the climate changes could be calculated as follows [23]: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the changes in the precipitation and potential evapotranspiration, respectively; [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are the sensitivity coefficient of runoff to precipitation and potential evapotranspiration, respectively: [figure omitted; refer to PDF] Similarly, for the hydrological sensitivity method, the runoff variation induced by human activities is the difference between the total runoff variation and the runoff variation induced by climate change.

4. Results and Discussion

4.1. Hydrological Regimes

First, precipitation and runoff data in the Taihu Lake Basin were analyzed. In Figure 2(a), the cumulative departure curve of the precipitation tended to decrease until 1979 and then increased. Therefore, 1979 could be the turning point for precipitation. Then, the Mann-Kendall test for precipitation showed a turning point in 1979 and verified the performance of the cumulative departure curve (Figure 2(b)). Similarly, the cumulative departure curve and the Mann-Kendall test of runoff both illustrated the turning point for runoff in 1982 (Figure 3).

Figure 2: Turning points of precipitation.

(a) Cumulative departure curve

[figure omitted; refer to PDF]

(b) Mann-Kendall test

[figure omitted; refer to PDF]

Figure 3: Turning points of runoff.

(a) Cumulative departure curve

[figure omitted; refer to PDF]

(b) Mann-Kendall test

[figure omitted; refer to PDF]

The inconsistency in the turning points for precipitation and runoff could be attributed to both climate changes and human activities. Here, the turning point of runoff divided the time series into the baseline period (1956~1981) and the modified period (1982~2008). In fact, the open policy of China exerted at the end of 1970s accelerated social and economic development, particularly in the Yangtze River Delta. Intense human activities began in the early 1980s in the most basins of China, including the Taihu Lake Basin. Moreover, the river system changed significantly, and the number of lakes decreased after the 1980s. The land use/cover distinctly changed in the 1980s due to social and economic development [24]. Thus, the turning point of 1982 was reasonable.

4.2. Assessment of the Causes of the Runoff Variation

The runoff, precipitation, and potential evapotranspiration increased by 46.7 mm, 51.4 mm, and 106.9 mm in the modified period compared with the baseline period. In Figure 4, the curve of the cumulative precipitation and potential evapotranspiration deviated in 1982, which also verified the reliability of the division for the baseline and modified period. The relationship in the baseline period was [figure omitted; refer to PDF] , which was applied to simulate the runoff in the modified period. In the double mass curve method, the results showed that human activities changed the runoff by 41.7 mm in the Taihu Lake Basin and contributed to 89% of the total runoff variation; climate changes contributed to 11% of the variation.

Figure 4: The double mass curve of precipitation and runoff in the Taihu Lake Basin.

[figure omitted; refer to PDF]

For the hydrological sensitivity method, the parameter [figure omitted; refer to PDF] was set to 0.5 [22]; then, [figure omitted; refer to PDF] and [figure omitted; refer to PDF] were computed as 0.80 and -0.46, respectively; 8.2 mm of the runoff variation was induced by climate changes. Consequently, the influences of the climate changes and human activities accounted for 18% and 82%, respectively, of the total runoff variation (Table 2). The error in the estimations by the double mass curve method and hydrological sensitivity method was 3.2 mm, with a deviation of 7%. Because of the relatively small error, the estimations could be used to verify the results. Therefore, 11~18% and 82~89% of the runoff variation were attributed to impacts of climate changes and human activities, respectively.

Table 2: Assessment of climate changes and human activities that impact the runoff variation in the Taihu Lake Basin.

Methods	Runoff variation induced by climate changes(mm)	Contribution of climate changes(%)	Runoff variation induced by human activities(mm)	Contribution of human activities(%)
Double mass curve method	5.0	11	41.7	89
Hydrological sensitivity method	8.2	18	38.5	82

Previous studies reported that human activities played a slightly dominant role upstream of the Taihu Lake Basin and Xitiaoxi River Basin [13, 25]. In this study, the results showed that human activities played an absolutely dominant role in the runoff variation in the Taihu Lake Basin. Specifically, the urbanization increased the water stage, and the hydrological processes significantly responded to the change in the land use/cover in the basin [14]. The river system was disrupted by intense human activities, and many hydraulic projects were constructed. Climate factors such as ENSO played a role in the precipitation in the basin. Moreover, the instability of the climate variability increases the flooding and rainfall in the basin [24].

The precipitation in HPJZ, HJHZ, WYZ, and HTZ during the modified period increased by 7.9%, 6.4%, 5.2%, and 2.3%, respectively, compared with the baseline period. The potential evapotranspiration changed the most in HPJZ (16.8%); in the other three zones, the change was approximately 12%. The runoff variation in HPJZ, WYZ, and HJHZ was 30.1%, 27.8%, and 20.2%, respectively, but it only varied by 1.3% in HTZ (Figure 5). The relationship between the cumulative precipitation and runoff in the baseline period is shown in Table 3. In HTZ, HJHZ, HPJZ, and WYZ, human activities contributed to 76~79%, 83~84%, 84~92%, and 95~97%, respectively, of the runoff variation. The impact of climate changes in the four zones was less than 24% (Figure 6).

Table 3: The relationship between the cumulative precipitation and runoff according to the two methods in the four zones.

Zone	Equations of the double mass curve method	Equations of the hydrological sensitivity method
Hangjiahu Zone	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
Huangpujiang Zone	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
Wuyang Zone	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
Huxi and Taihu Zone	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]

Figure 5: The changes in the hydrological variables in the four zones.

[figure omitted; refer to PDF]

Figure 6: Quantitative assessments of the attribution to runoff variations in the four zones.

(a) Hangjiahu Zone

[figure omitted; refer to PDF]

(b) Huangpujiang Zone

[figure omitted; refer to PDF]

(c) Wuyang Zone

[figure omitted; refer to PDF]

(d) Huxi and Taihu Zone

[figure omitted; refer to PDF]

In the four zones, human interferences remarkably dominated the runoff variation. Human activities in WYZ were most intense, with an impact greater than 95%; the lowest impact occurred in HTZ. The relationship between runoff and precipitation was closer in HTZ than in other three regions (Table 3). The largest coefficient of the double mass curve method and the hydrological sensitivity method occurred in HTZ, while the smallest values occurred in WYZ. In fact, the main land cover was water bodies in HTZ, where the urbanization was weakest. However, WYZ had the most intense urbanization [26], which corresponded to the greatest human activities.

The limitations of this study include uncertainties and the simple hypothesis of an independent relation between climate changes and human activities. The general impacts of climate changes and human activities were assessed. The specific driving forces that impact the runoff variation will be analyzed in the future.

5. Conclusion

The Taihu Lake Basin suffers disturbances from climate changes and human activities. The detection and identification of runoff variations are significant for countering water resources issues in the basin. The study aims to quantify the driving forces of runoff variations in the Taihu Lake Basin. The conclusions can be summarized as follows:

(1) Based on the cumulative departure method and Mann-Kendall method, the runoff in the Taihu Lake Basin abruptly changed in 1982. Thus, 1982 was considered the turning point between the baseline period (1956~1981) and the modified period (1982~2008).

(2) The impacts of climate changes and human activities were estimated by the double mass curve method and the hydrological sensitivity method; these factors contributed to 11~18% and 82~89%, respectively, of the runoff variation in the basin. Human activities significantly dominated in the four zones, among which WYZ had the most intense human activities (95~97% contribution rate). The intense urbanization and remarkable land use/cover change in the Taihu Lake Basin could be the main human activities that contribute to hydrological alterations.

Acknowledgments

The authors would like to thank Mrs. Hejuan Lin of the Taihu Basin Authority, Ministry of Water Resources, China, for her advice and assistance with the data. This study is financially supported by the Key Technologies R & D Program of China (Grant No. 2012BAC21B02).

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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